1. Field of the Invention
The present invention relates generally to a method of and apparatus for analyzing medical claims data, and more particularly to a method of and apparatus for integrating medical claims data, including, inpatient, outpatient, ambulatory, clinical laboratory, and prescription drug claim data, from one or more sources for comparative analysis at the network level, provider level, or patient level. The invention organizes the claims into clinically related diagnostic clusters, and enables claims with missing or incorrect diagnostic codes to be categorized into such diagnostic clusters in order to better estimate the overall cost of patient treatment and to measure the efficiency of treatment at various levels.
2. Description of the Related Art
The costs of health care today are rapidly increasing as the health care industry becomes more complex, specialized and sophisticated. Over the years, the delivery of health care services has shifted from local physicians to large managed health care organizations. This shift reflects the growing number of medical specialties, and the complexity and variety of health care options and programs. This complexity and specialization has created large administrative systems that coordinate the delivery of health care between health care providers, administrators, patients, payers and insurers. The cost of supporting these administrative systems has been steadily rising, contributing to today's rising costs of health care.
Beginning in the late 1970's, the precipitous increase in medical care costs prompted health insurance companies to evaluate insurance claims data to determine the reasons behind this increase. The first-generation claims data evaluation systems that were developed generally concentrated on analyzing medical charges and utilization in the hospital inpatient setting. As a result of these evaluations, health insurers implemented pre-certification utilization review programs to control unnecessary inpatient admissions and days, and employers turned to preferred provider organizations to manage per-day charge increases.
First-generation data claims analysis, however, was short lived. After Medicare implemented the Prospective Payment System (PPS) in 1983, the number of hospital in-patient days significantly decreased for both the Medicare and non-Medicare populations. The PPS provided physicians with financial incentive to treat certain patients in an ambulatory setting. As more health care dollars were spent on ambulatory services, employers and health insurers realized that both hospital and ambulatory treatment had to be evaluated to determine the principal reasons for health care cost increases. Thus, insurance carriers and consultants developed second-generation data analysis systems focusing on ambulatory utilization and charges.
Most second-generation data claims analysis systems currently evaluate utilization experience on a basis of per-1,000-covered-individuals (i.e., employees and their dependents) and charges on a per-service basis. For example, common utilization rates include the number of hospital admissions, physician office visits, and prescription drug fills per 1,000 individuals. Charge rates consist of charges per hospital day, per X-ray/diagnostic testing service, and per physician office visit.
Using second-generation systems, gross overutilization and significant excess charge patterns can be identified in both hospital and ambulatory care settings. For instance, if the number of prescription drugs prescribed per 1,000 covered individuals is 50 percent greater than an expected value, the health network can be reasonably sure that it has a problem with prescription drug utilization. For various reasons, however, these second-generation systems are not very accurate at assessing a health plan's overall cost efficiency. By cost efficiency is meant that a plan's overall providers treat medical conditions with the least expensive level of medical care possible and still achieve the desired health outcome for the patient.
Second-generation systems generally do not combine and evaluate all hospital and ambulatory services incurred for treating an individual's medical condition. Instead, these systems divide individual's claims into 20 to 25 different utilization and charge categories. Each category is then analyzed separately to determine whether there is a problem with utilization per 1,000 individuals, charges per service, or both. If problem areas are identified, health care networks develop specific cost-control strategies to contain future benefit increases.
This process, however, is a piecemeal approach for evaluating the cost efficiency of a health plan, and health care networks may draw wrong conclusions from such an analysis. For example, assume that a network determines that the number of physician office visits per 1,000 individuals is 30 percent higher than an expected value. A logical conclusion is that the health plan's physicians are not practicing cost-efficient medicine. Yet if the health plan observes that the number of hospital admissions is 20 percent lower than expected, a different conclusion may be reached: perhaps, for example, physicians are practicing cost-efficient medicine by treating patients in the ambulatory setting whenever possible. The health plan must then make a subjective assessment as to whether a 30 percent higher-than-expected physician office visit rate is acceptable or too high given the low hospital admission rate.
Consequently, a need exists for a system to enable health care networks to analyze their medical claims/encounter data and to more accurately evaluate the overall cost efficiency of the health plan at the network level, at the provider level, and at the patient level.
The American Medical Association in conjunction with the Health Care Financing Administration developed a system of codes for the purpose of describing physician work for medical and surgical procedures, diagnostic tests, laboratory studies, and other physician medical services rendered to clients. This system of codes is generally referred to as Current Procedural Terminology, or CPT, codes. They provide a uniform language that details medical, surgical, and diagnostic services utilized by physicians to communicate to third-party payers the services that are rendered.
The World Health Organization developed a similar method to identify diseases, injuries, impairments, symptoms, medical procedures and causes of death. These codes are International Classification of Diseases 9th edition Clinical Modification (ICD.9) codes. The ICD coding system was designed for the classification of morbidity and mortality information for statistical purposes and for the indexing of hospital records by disease and operations for data storage and retrieval. The ICD codes are initially divided into Disease and Procedure sections. These sections are further divided into subsections which encompass anywhere from 1-999 three digit disease or 1-99 two digit procedure code categories. Within the three digit code categories there can be an additional 1 or 2 decimal digits to divide the codes into subcategories which further define the disease manifestations and/or diagnostic procedures. There are approximately 15,000 ICD.9 codes. It will be appreciated that while the present invention will be described specifically with respect to ICD.9 coding, the invention is applicable to future versions of the ICD classification system, and may be modified to operate with other types of classification systems as well.
Billing for a physician's services has become increasingly more complex in recent years. Medicare requires that a code be assigned to each patient encounter, the interaction between a patient and the physician, assistant, nurse or other health care provider to evaluate the patient's medical problem. One problem that has arisen is that physician's may submit medical claims with improper or missing diagnosis codes. This makes it difficult in assessing overall treatment charges to include the charge data for those claims that are improperly coded. Accordingly, it would be desirable to have a system that organizes medical claims data into clusters in which all claims related to a medical condition, including those claims that are improperly coded and drug claims, are grouped so that it is possible to ascertain the overall costs of treating the medical condition.
Another problem that has arisen due to changes in the medical field is that of assessing the overall charges attributable to general physician's, who serve as gatekeepers to specialist services in HMO's. For example, assuming that a general practitioner treating a diabetic too often refers to patient to specialized care. Without attributing the charges of the specialist to the general physician, the overall costs for treating the patient that are attributable directly to the general physician will actually be lower than average, whereas if the charges of the specialist are included, the overall cost of treating the patient will be higher than average. Accordingly, it would be desirable to have a system that organizes medical claims data into clusters based upon the medical condition, and that enables all claims related to a medical condition to be attributable to a gatekeeper physician.
A further problem in assessing the overall costs of medical treatment is that certain claims, e.g., prescription drug claims, do not include ICD.9 codes. In some cases claims use alternative codings systems, such as GPI (Generic Product Identifier) codes, which are used to code prescription drug claims, and CPT codes. Accordingly, it would be desirable to have a medical claims analysis system that is capable of categorizing uncoded claims, or claims in alternative coding systems, with the ICD.9 coded claims related to treatment of the underlying medical conditions that necessitated the uncoded or alternatively coded claims.
Various systems have been patented in the medical billing field. Such systems are shown, for example, in U.S. Pat. Nos. 5,018,067; 5,359,509; 4,667,292; 5,483,443; 5,307,262; and 5,253,164. None of these systems, however, overcomes the aforementioned problems with respect to assessing a health plan's true cost efficiency, assessing the overall costs of treatment of medical conditions, and allocation of medical costs to physicians to assess the efficiency of treatment by the physicians.